Resident Plug-In Electric Vehicle Charging Modeling and Scheduling Mechanism in the Smart Grid

With the development of smart grid and the increase of global resident Plug-In Electric Vehicle (PEV) market in the near future, the interaction between limited distribution grid capacity and uncontrollable PEV charging loads can lead to violations of local grid restrictions. And the proper model charging scheduling mechanism is the key to assess and satisfy various resident charging requirements and help in optimizing utility utilization. In this paper, the distribution grid profile model with PEV charging power is firstly constructed for the purpose of studying resident PEV charging impact on the distribution grid. To better reflect the actual impact of PEVs, we use real data on driving behaviors, vehicle characteristics, and electricity loads to generate our model. Furthermore, an improved queuing-theory-based scheduling mechanism is proposed, the distribution grid communication structure and the algorithm are illustrated, and computer simulations are demonstrated to verify their performance. The results show that the proposed scheduling mechanism will enhance the distribution grid flexibility to meet various charging requirements while maximizing the grid capacity.

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Negative Impacts on the Smart Distribution Grid Caused by New Energy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1092-1093.463 ◽

2015 ◽

Vol 1092-1093 ◽

pp. 463-466

Author(s):

Li Jun Qin ◽

Wan Tao Yang

Keyword(s):

Smart Grid ◽

Electric Vehicle ◽

Distribution Network ◽

Distribution Grid ◽

Electric Vehicle Charging ◽

New Energy ◽

Vehicle Charging ◽

Smart Distribution Grid ◽

Charging Stations ◽

The Impact

The access problem of new energy is one of the core content of the smart grid. New energy such as wind, solar, electric vehicle charging stations have strong intermittent and fluctuations. Their technical performance is poor to access to the grid.They inject harmonics into grid and have other issues. This paper describes the impact on the distribution network after the wind, photovoltaic power generation, electric vehicle charging stations connected to the grid.It is hoped that you can build smart grid distribution model,research in-depth and analysis the combined effects of smart distribution grid after new energy sources accessed, to achieve the monitoring of the various components of the distribution network and the best combination for electricity.

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The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419590274 ◽

2019 ◽

Vol 33 (08) ◽

pp. 1959027 ◽

Cited By ~ 1

Author(s):

Jing-min Wang ◽

Yan Liu ◽

Yi-fei Yang ◽

Wei Cai ◽

Dong-xuan Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Robust Optimization ◽

Electric Vehicle ◽

Optimization Algorithm ◽

Site Selection ◽

Queuing Theory ◽

Location Model ◽

Electric Vehicle Charging ◽

Charging Stations ◽

Charging Demand

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

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